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After Sandy Hook, gun rush led to 60 additional accidental deaths

Data: Levine & McKnight, Science, 2017 DOI:11 etc.; Note: Death rate deviation data is December of previous year to April of current year; Chart: Axios Visuals

In the five months after the mass shooting at Sandy Hook Elementary School, gun sales rose and people took their guns out of storage. This exposure led to at least 60 more accidental deaths than would otherwise have happened — and 20 of them were children, according to a new study published Thursday in the journal Science.

The details: These charts show monthly changes away from the expected seasonal rate of gun purchases and accidental firearm deaths in children. Following Sandy Hook, both spike dramatically.

"This event should have awakened people to what can we do in our society, but too many people took the opposite tact and caused more harm to themselves and others," David Hemenway, who conducts research on injury prevention at Harvard and was not involved in the study, tells Axios.

What they did: The researchers calculated the average rate of accidental firearm deaths for adults and children in the United States from 2008-2015, and measured deviations from that rate. They compared that to data on background checks, Google searches for 'buy gun' as a proxy for gun sales and searches for 'clean gun' to account for people taking their guns out of storage.

Finally, they broke the national data down state-by-state to check that the relationship between mass shootings, gun purchases, and gun deaths wasn't coincidental. Because the trend was true in each individual state, and not just in the national average, the association was stronger.

What they found: Background checks and Google searches for buying guns and about gun maintenance increased following Sandy Hook, indicating increased gun exposure — the rush stopped when the legislation failed . A large jump in accidental deaths in both adults and children occurred during that time. Then, as people learned how to use their guns or put them into storage, death rates returned to normal.

"It's really about exposure," says study author Phillip Levine, an economist at Wellesley College. "Regardless of how many guns there are, if they're all stored properly, the risk of accidental deaths is limited. It has to be about what's occurring that's leading them to not be stored properly at that moment."

What's happening: After mass shootings, particularly ones that raise the specter of gun control legislation, it's well documented that gun purchases rise, though the trend appears to have stopped since the election of a congress and president that are against gun control. This is one of the first studies to link those legislative battles and gun sale trends to accidental deaths.

Yes, but: There are a lot of factors at play during these watershed events, so it's difficult to put the blame solely on discussions of gun control, says Hemenway.

What's next: Levine would like to parse out the long-term effects of these gun purchases. There's little evidence of an increase in murders after shootings, but it makes sense to assume that more guns could lead to more murders or gun-involved domestic violence in the long run. But because so many other factors influence gun violence, it's extremely difficult to sort out any trend, says Levine.

Hemenway would like to see research into the impacts of multiple guns in a household. "The difference between 0 and 1 is enormous. Between 1 and 5, we just don't know."

A Catch-22: There are proven ways to reduce gun violence, notes economist and sociologist Philip Cook in policy piece that ran with the study. Concealed carry laws, laws that ban those convicted of domestic abuse from purchasing guns, and extended sentences aimed at curbing armed robbery all appear to measurably reduce gun violence. But in the initial act of passing such legislation, it's possible gun deaths may temporarily go up.

Despite this, "I don't think one should take away that you shouldn't bother trying," says Levine.

Early humans innovated tools earlier than thought

Unpredictable climate and natural disasters like earthquakes may have spurred early humans to create innovative tools and ways to communicate earlier than previously thought, according to 3 studies published Thursday in Science.

What they found: Evidence that around 320,000 years ago — near the start of the Middle Stone Age (MSA) and tens of thousands of years earlier than previous evidence has shown — early humans in East Africa may have created projectile hunting tools, developed ways to communicate using colors for mapping or identification purposes, and traveled longer distances to trade, hunt or obtain valuable materials.

"It's not just humans changing but really the entire ecosystem. It's a picture that's bigger than just the human ancestors themselves."

Yonatan Sahle from Tubingen University told The Atlantic that different parts of Africa may have had varied timing when MSA first appeared, how much it overlapped with the older Acheulean tech, and whether it occurred together with Homo Sapiens fossils.

Why it matters:

"This is surely a landmark study. The work at Olorgesailie is most welcome as plaeoanthropologists have very little information about the habitats and behaviors at 320,000 years ago, a critical time period in human evolution... the rigorous work at Olorgesailie fills a a gap in our knowledge about environments and human behaviors at this critical time. It is very rare to have well-dated stone tools in association with animal remains and environmental information."

— Michael Petraglia, from the Max Planck Institute for the Science of Human History, Germany, who was not part of the studies

Environmental triggers: The scientists believe dramatic changes in the environment like periods of intense rain or drought, earthquakes, and altered animal communities pushed early humans to travel greater distances for food, find new super-sharp obsidian rocks to use for tools and pigments to use for communication, and locate other communities of early humans with whom to trade.

"[T]he behavioral hallmarks of the MSA they observe — refined tool manufacture, wider mobility and foraging, pigment use, implied social networks — occurred during a time of enhanced environmental variability. As with modern hunter gatherer populations, these are anticipated behavioral and cultural responses to greater environmental variability and food insecurity."

— Columbia University's Peter de Menocal, who was not part of the study

Innovative tools and weapons: Prior to this time period, early humans had been using rudimentary hand axes and cleavers to hunt animals and as weapons.

The teams discovered large amounts of non-native obsidian rock in the basin. The obsidian, which is extremely sharp when fractured, was traced back to their origin at locations up to 55 miles away through very rugged terrain.

Prior to this, 99% of their rocks were obtained within 5 km — this is a "radical change," Potts says.

What it means: "The transfer of obsidian from long distances is essentially the first evidence of trade," Potts says.

The smaller shapes and modification at the base suggests the pointed tips were hafted to wood or bone — essentially the original projective weapon.

"What can you say — the world hasn't been the same since [the discovery of projective weapons]," Potts says.

Pigments: The teams found red, green and black pigments used in various locations. "Pigments are evidence of symbolic communication," Potts says. It can be used to stain the hair or skin to show if you are "friend or foe." It could also be used to mark the best way to get somewhere on a map — or warn people off a particular territory.

"The possible production of red pigments is especially exciting, as this may imply that the humans at Olorgesailie were cognitively advanced. This is some of the best, and earliest evidence for pigment use in the archaeological record, reaffirming that at the outset, Homo sapiens was a symbolic species," Petraglia says.

What's next: Research is lacking for a chronological gap — the time period between 500,000 years ago and 320,000 years ago. Potts says his team is currently working to discover more about that period.

Yejin Choi: Trying to give AI some common sense

Photo illustration: Axios Visuals

Artificial intelligence researchers have tried unsuccessfully for decades to give machines the common sense needed to converse with humans and seamlessly navigate our always-changing world. Last month, Paul Allen announced he is investing another $125 million into his Allen Institute for Artificial Intelligence (AI2) in a renewed effort to solve one of the field's grand challenges.

Axios spoke with Yejin Choi, an AI researcher from the University of Washington and AI2 who studies how machines process and generate language. She talked about how they're defining common sense, their approach to the problem and how it's connected to bias.

How do you define common sense?

"Common sense is fairly trivial everyday knowledge that we have about people and about the world. It's knowledge about how the world works — how people think, what motivates them, how they act, and why they do what they do."

"Imagine there's a robot in your household in the future, and you want to store leftover pie in a container. The robot should pick a container that's large enough to store that pie, and today that spatial reasoning relative to physical properties of different objects in the world and how you interact with them are not quite well represented in these system models."

Why common sense poses a challenge to machines:

"We have a world model in our mind when we do daily operations. AI systems today, despite tremendous advancement in recent years, they are not very good at generalizing out of pure example, so they tend to be very, very task specific, and very domain specific."

"A machine translation system may seem like it understands some language enough to translate into another language, but actually there's not that much understanding happening, per se, because that syntax knowledge cannot be reused for making very trivial small talk with a human, for example."

Their approach:

"We have this commonsense knowledge without our parents or teachers having to enumerate all of it one by one. Nobody told us that elephants are usually bigger than butterflies, however we can reason about it. You ask me that question, I can think about it, and I can answer that question even though I've never seen that statement explicitly written anywhere."

"We're taking a similar approach. It may be possible that we can learn to answer these sort of questions, even including those that we've never seen before. That's fundamentally the ability that AI systems needs to have — dealing with unknowns and previously unseen situations."

What data is needed to make common sense models?

"There is a paper called Verb Physics, and in that work the dataset is basically a combination of a lot of natural language documents — a huge corpus of how people use language and from that we look for patterns. For example, what kind of things do I throw? What kind of things do I enter into? I enter my house. I exit my house. And, that sort of implies that my house must be bigger than me for me to enter into and exit from."

"So, we can infer different action dynamics, preconditions, and post conditions — all those different physical objects, for me to do some action involving them."

"The short term goal is to develop a common sense benchmark dataset. Then, the ultimate goal is to acquire knowledge that's good enough to do well in that benchmark dataset. That's step one."

The issue of AI and human bias:

"We showed [in a study of movie scripts last year] how women in movies carry much less power compared to men. It's the kind of actions that they do and the kind of language they use when they speak. Men usually fight and they do stuff, they save the world. Women, on the other hand, they tend to wait, they are being watched, and they look pretty. What they do tends to be pretty passive."

"It's one of my passions to develop AI technology that can detect all these biases in humans and also, ideally, be able to correct them in the future."

How is bias connected to common sense?

"These are connected in that the way bias is coming across, often times can be inclusive or implied. Current models are much better at understanding what's explicitly stated, but less good at anticipating what's not said. It's good to be catching some of the explicit biases, but it's important to also detect all of the implied ones because that still influences us. The ability to read between the lines ultimately is what requires common sense, so that's the connection between the two."